Biological Systems Engineering Lab

  • Biological Systems Engineering Lab
  • Research Groups
  • BIO-REMOTE
  • BIO-REMOTE
  • Biweekly lab seminar 1
  • Biweekly lab seminar 2
  • Biweekly lab seminar 3
  • Biweekly lab seminar 4
  • Biweekly lab seminar 5
  • Biological Systems Engineering Lab
  • Research Groups
  • BIO-REMOTE
  • BIO-REMOTE
  • Medical engineering group Seminar 1
  • Medical engineering group Seminar 2
  • Kansei brain group Seminar 1
  • Kansei brain group Seminar 2
  • Kansei brain group Seminar 3
  • Kansei brain group Seminar 4
  • Human modeling group Seminar 1
  • Human modeling group Seminar 2
  • Human modeling group Seminar 3
  • Human modeling group Seminar 4
  • Biological signal analysis group seminar 1
  • Biological signal analysis group seminar 2
  • Biological signal analysis group seminar 3
  • Biological signal analysis group seminar 4
  • Biological signal analysis group seminar 5
  • Biological signal analysis group seminar 6
  • Biological signal analysis group seminar 7
  • Biological Systems Engineering Lab
  • Research Groups
  • BIO-REMOTE
  • BIO-REMOTE
  • Biweekly lab meeting 4
  • Biweekly lab meeting 4
  • Biweekly lab meeting 4
  • Biweekly lab meeting 4
  • Biweekly lab meeting 4
  • Biweekly lab meeting 4
  • Biweekly lab meeting 4
  • Biological Systems Engineering Lab
  • Research Groups
  • BIO-REMOTE
  • BIO-REMOTE
  • Laboratory group photo 1
  • Laboratory group photo 2
  • Biological Signal Analysis Group
  • Medical engineering group
  • Human modeling group
  • Kansei brain group
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Living organisms developed in nature through the evolution process are equipped with supremely skilled and sophisticated biological functions that cannot be realized with current engineering techniques. Analysis of these mechanisms may lead to not only elucidation of biological functions but also development of a wide variety of novel engineering systems.

From the viewpoint of a scientist approaching the secrets of living organisms and from that of an engineer developing machinery useful for human kind, the members of Biological Systems Engineering laboratory work on a wide variety of projects to analyze the characteristics of biological functions from theoretical and experimental approaches employing engineering techniques aiming to find new principles peculiar to biological systems, and develop novel medical/welfare apparatuses and industrial devices by applying the elucidated principles.

Through such research activities, the students can learn in-depth knowledge about biological systems based on electricity, electronics, systems and information engineering foundation allowing themselves to become creative engineers capable of seeking a new principle and expanding it into new fields.

Five research themes

There are still a lot of unknown functions and mechanisms hidden in the biological system. If we can elucidate and utilize them from engineering standpoint,then there is a possibility of creating new technologies to carve out the future of the 21st century. The Biological Systems Engineering Laboratory categorizes the broad research field of biological systems into five major research themes in order to explore specific research projects under each theme, and further functionally coordinate and fuse each theme to create novel research fields.

Biological signal analysis and its application to human interfaces

We develop novel signal processing algorithms that enablethe interpretation of human motions, intentions, and physiological/psychological states contained in biological signals, such as myoelectric signals, electroencephalograms, and electrocardiograms, as well as create robotic interfaces and medical welfare equipment.

Biomechanical analysis and its application to human-machine system design

We model human sensory/motor functions from electrical and electronicperspectives based on experimentally measured data, and develop novel movement support systems and next-generation automobile control systems by incorporating modeled human characteristics.

Statistical structure of neural networks based on novel machine learning algorithms

We propose new machine learning algorithms and neural networks based on probabilistic statistical theory and applythese to the development oflearning and control technologiesfor robots, medical welfare equipment, and medical data classificationtechnology.

Brain function/neural network modeling and artificial life models

Focusing on functions such as locomotion generation, sensation, perception, learning, and judgment, we model brain functions from an engineering viewpoint using artificial neural networks. Ultimately, we aim to model and analyze higher brain functions, especially social brain functions that understand the minds of others and live harmoniously, and Kansei that involves nonverbal, unconscious, and intuitive sensibilities. We also develop artificial life form models based on biological knowledge using the constructed brain models.

Biometric information mining technology and medical support systems

We are engaged in the research and development of novel medical support systems and medical devices through medicine-engineering collaborations by utilizing electric and electronic systems and information engineering technologies, such as biomechanical analysis technology, biological signal analysis technology, machine learning technology, and biological simulation technology that were developed in the Biological Systems Engineering laboratory.

Publications

Our research results have been published in scientific journals, books, conference proceedings, patent, etc.. The numbers of publications the lab produced are shown as follows
(as of May 28, 2024):

Latest papers

Finger-tapping Motion Recognition Based on Skin Surface Deformation Using Wrist-mounted Piezoelectric Film Sensors

Shumma Jomyo, Akira Furui, Tatsuhiko Matsumoto, Tomomi Tsunoda, and Toshio Tsuji
IEEE Sensors Journal, doi: 10.1109/JSEN.2024.3386333 (Accepted, SCI, IF=4.3)
Early Access

Simultaneous Motion and Respiration Measurement in Adult Zebrafish Based on the Instantaneous Spatial Distribution of Separated Ventilatory Signals

Zu Soh, Saki Maruko, Manaru Fujita, Shin Wakitani, Masayuki Yoshida, and Toshio Tsuji
IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-9, 2024, Art no. 9509209, doi: 10.1109/TIM.2024.3387502. (SCI, IF=5.6)
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Correlation analysis between echinocytosis stages and blood viscosity during oxygenator perfusion: an in vitro study

Shigeyuki Okahara, Satoshi Miyamoto, Zu Soh, Masaru Yoshino, Hidenobu Takahashi, Hideshi Itoh, and Toshio Tsuji
ASAIO JOURNAL, DOI: 10.1097/MAT.0000000000002214, April 18, 2024 (online first, SCI, IF=4.2)
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Neuroimaging-based Evidence for Sympathetic Correlation between Brain Activity and Peripheral Vasomotion during Pain Anticipation

Ziqiang Xu, Zu Soh, Yuta Kurota, Yuya Kimura, Harutoyo Hirano, Takafumi Sasaoka, Atsuo Yoshino, and Toshio TsujiScientific Reports, volume 14, Article number: 3383, doi.org/10.1038/s41598-024-53921-4, Published online: 09 February 2024. (SCI, IF=4.6) URLPDF

The number of microbubbles generated during cardiopulmonary bypass can be estimated using machine learning from suction flow rate, venous reservoir level, perfusion flow rate, hematocrit level, and blood temperature

Satoshi Miyamoto, Zu Soh, Shigeyuki Okahara, Akira Furui, Taiichi Takasaki, Keijiro Katayama, Shinya Takahashi, and Toshio TsujiIEEE Open Journal of Engineering in Medicine and Biology, Volume: 5, pp. 66-74, DOI: 10.1109/OJEMB.2024.3350922, Date of Publication: 08 January 2024. (ESCI, IF=5.8)
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Noninvasive Characterization of Peripheral Sympathetic Activation across Sensory Stimuli Using a Peripheral Arterial Stiffness Index

Ziqiang Xu, Reiji Anai, Harutoyo Hirano, Zu Soh and Toshio Tsuji
Frontiers in Physiology (Computational Physiology and Medicine), 14:1294239, DOI: 10.3389/fphys.2023.1294239, PUBLISHED 08 January 2024. (SCI, IF=4.0)
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Repetitive pain experiences modulate feedforward control of hemodynamics and modification by nitrous oxide/oxygen inhalation in humans

Hironori Miyazaki, Yoshifumi Nishio, Kota Miyahara, Chiaki Furutani, Ziqiang Xu, Noboru Saeki, Toshio Tsuji, and Yoshiyuki Okada
Heliyon,  Volume 9, ISSUE 12, e23121, doi: 10.1016/j.heliyon.2023.e23121, Published: November 29, 2023. (SCI, IF=4.0)
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Vertical Dynamic Visual Acuity is Significantly Lower than Horizontal Dynamic Visual Acuity

Aoi Tachihara, Zu Soh, Tomohiko Mizuguchi, Akihiko Kandori, Seiji Hama, and Toshio Tsuji
Scientific Reports, volume 13, Article number: 20999, doi:10.1038/s41598-023-48292, Published online: 28 November 2023. (SCI, IF=4.6)
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Prediction of blood pressure changes during surgical incision using the minimum evoked current of vascular stiffness value under sevoflurane anesthesia

Daiki Shorin, Satoshi Kamiya, Ryuji Nakamura, Ayaka Ishibashi, Noboru Saeki, Toshio Tsuji, and Yasuo M. Tsutsumi
Scientific Reports, volume 13, Article number: 20486, doi:10.1038/s41598-023-46942-y, Published online: 22 November 2023. (SCI, IF=4.6)
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Representative papers

Human Hand Impedance Characteristics during Maintained Posture in Multi-Joint Arm Movements

T. Tsuji, P. Morasso, K. Goto, and K. Ito
Biological Cybernetics, Vol.72, pp.475-485, 1995.

A Log-Linearized Gaussian Mixture Network and Its Application to EEG Pattern Classification

T. Tsuji, O. Fukuda, H. Ichinobe, and M. Kaneko
IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews, Vol. 29, No. 1, pp. 60-72, February 1999.

A Recurrent Log-linearized Gaussian Mixture Network

T. Tsuji, N. Bu, M. Kaneko, and O. Fukuda
IEEE Transactions on Neural Networks, Vol.14, No.2, pp.304-316, March 2003.

A Human-Assisting Manipulator Teleoperated by EMG Signals and Arm Motions

O. Fukuda, T. Tsuji, M. Kaneko and A. Otsuka
IEEE Transactions on Robotics and Automation, Vol.19, No.2, pp.210-222, April 2003.

Quantitative Evaluation of Pain during Electrocutaneous Stimulation using a Log-Linearized Peripheral Arterial Viscoelastic Model

H. Matsubara, H. Hirano, H. Hirano, Z. Soh, R. Nakamura, N. Saeki, M. Kawamoto, M. Yoshizumi, A. Yoshino, T. Sasaoka, S. Yamawaki, and T. Tsuji
Scientific Reports, volume 8, Article number: 3091, doi:10.1038/s41598-018-21223-11, Published online: 15 February 2018.

Continuous Blood Viscosity Monitoring System for Cardiopulmonary Bypass Applications

S. Okahara, Z. Soh, S. Miyamoto, H. Takahashi, S. Takahashi, T. Sueda, and T. Tsuji
IEEE Transactions on Biomedical Engineering, Vol.64, No.7, pp. 1503-1512, DOI:10.1109/TBME.2016.2610968, JULY 2017.

Assessment of Lower-limb Vascular Endothelial Function Based on Enclosed Zone Flow-mediated Dilation

H. Hirano, R. Takama, R. Matsumoto, H. Tanaka, H. Hirano, Z. Soh, T. Ukawa, T. Takayanagi, H. Morimoto, R. Nakamura, N. Saeki, H. Hashimoto, S. Matsui, S. Kishimoto, N. Oda, M. Kajikawa, T. Maruhashi, M. Kawamoto, M. Yoshizumi, Y. Higashi, and T. Tsuji
Scientific Reports, volume 8, Article number: 9263, doi:10.1038/s41598-018-27392-3, Published online: 18 June 2018.

A computational model of internal representations of chemical gradients in environments for chemotaxis of Caenorhabditis elegans

Z. Soh, K. Sakamoto , M. Suzuki , Y. Iino, and T. Tsuji
Scientific Reports, volume 8, Article number: 17190, doi:10.1038/s41598-018-35157-1, Published online: 21 November 2018.

A Scale Mixture-based Stochastic Model of Surface EMG Signals with Variable Variances

A. Furui, H. Hayashi, and T. Tsuji
IEEE Transactions on Biomedical Engineering, DOI: 10.1109/TBME.2019.2895683, Date of Publication: 28 January 2019.

A myoelectric prosthetic hand with muscle synergy-based motion determination and impedance model-based biomimetic control

A. Furui, S. Eto, K. Nakagaki, K. Shimada, G. Nakamura, A. Masuda, T. Chin, and T. Tsuji
Science Robotics, Vol. 4, Issue 31, eaaw6339, DOI: 10.1126/scirobotics.eaaw6339, 26 June 2019.

Markerless Measurement and Evaluation of General Movements in Infants

T. Tsuji, S. Nakashima, H. Hayashi, Z. Soh, A. Furui, T. Shibanoki, K. Shima, and K. Shimatani
Scientific Reports, volume 10, Article number: 1422, doi:10.1038/s41598-020-57580-z, Published online: 29 January 2020.